def bulid_TIs_dataset(stock_symbol,
start_date,
end_date,
window,
normalize=True):
cols = ["Date", "Adj Close"]
df = get_stock_data(stock_symbol, start_date, end_date, cols)
df.rename(columns={"Adj Close": 'price'}, inplace=True)
df['momentum'] = dpp.compute_momentum_ratio(df['price'], window)
df['sma'] = dpp.compute_sma_ratio(df['price'], window)
df['bolinger_band'] = dpp.compute_bollinger_bands_ratio(
df['price'], window)
df['actual_price'] = df['price']
df = df[window:]
scaler = None
if normalize:
scaler = MinMaxScaler()
df['price'] = scaler.fit_transform(df['price'].values.reshape(-1, 1))
df['momentum'] = scaler.fit_transform(df['momentum'].values.reshape(
-1, 1))
df['sma'] = scaler.fit_transform(df['sma'].values.reshape(-1, 1))
df['bolinger_band'] = scaler.fit_transform(
df['bolinger_band'].values.reshape(-1, 1))
df['actual_price'] = scaler.fit_transform(
df['actual_price'].values.reshape(-1, 1))
print(df.head())
print(df.tail())
return df, scaler
def bulid_new_TIs_dataset(stock_symbol,
start_date,
end_date,
window,
normalize=True):
cols = ["Date", "Adj Close", "Volume"]
df = get_stock_data(stock_symbol, start_date, end_date, cols)
df.rename(columns={"Adj Close": 'price'}, inplace=True)
df['momentum'] = dpp.compute_momentum_ratio(df['price'], window)
df['sma'] = dpp.compute_sma_ratio(df['price'], window)
df['bolinger_band'] = dpp.compute_bollinger_bands_ratio(
df['price'], window)
df['volatility'] = dpp.compute_volatility_ratio(df['price'], window)
df['vroc'] = dpp.compute_vroc_ratio(df['Volume'], window)
df['actual_price'] = df['price']
df.drop(columns=["Volume"], inplace=True)
df = df[window:]
df.replace([np.inf, -np.inf], np.nan, inplace=True)
df.fillna(method='ffill', inplace=True)
df.fillna(method='bfill', inplace=True)
scaler = None
if normalize:
scaler = MinMaxScaler()
df['price'] = scaler.fit_transform(df['price'].values.reshape(-1, 1))
df['momentum'] = scaler.fit_transform(df['momentum'].values.reshape(
-1, 1))
df['sma'] = scaler.fit_transform(df['sma'].values.reshape(-1, 1))
df['bolinger_band'] = scaler.fit_transform(
df['bolinger_band'].values.reshape(-1, 1))
df['volatility'] = scaler.fit_transform(
df['volatility'].values.reshape(-1, 1))
df['vroc'] = scaler.fit_transform(df['vroc'].values.reshape(-1, 1))
df['actual_price'] = scaler.fit_transform(
df['actual_price'].values.reshape(-1, 1))
print(df.head())
print(df.tail())
return df, scaler